Dynamic checkout page optimization to forestall negative user action

ABSTRACT

In an example embodiment, a method for processing payments made via an electronic payment processing system is provided. An example method includes obtaining training data from a data source. The training data relates to prior purchases made via the electronic payment processing system, wherein the data source includes, in some examples, only a checkout page in a purchase transaction funnel. Features associated with a negative user action in relation to prior purchases are identified. A machine learning algorithm produces a dynamic transactional behavior score indicative of a probability that a purchase will invoke a negative user action.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior U.S. Application No.16/274,043, filed on Feb. 12, 2019, which is incorporated by referenceherein in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the technicalfield of special-purpose machines that facilitate adding new features toa payment processor or payment device. The subject matter also relatesto an improved payment processor that implements such new features andincludes software-configured computerized variants of suchspecial-purpose machines and improvements to such variants, and to thetechnologies by which such special-purpose machines become improvedcompared to other special-purpose machines that facilitate adding thenew features.

Aspects of the present disclosure use data signals uniquely observablefrom a checkout page of a purchase transaction funnel to make inferencesabout a customer's experience in the transaction. In one aspect, animproved payment processor can dynamically optimize a checkout pageusing a machine-learned model.

BACKGROUND

The present subject matter seeks to address technical problems existingin conventional payment processors and systems. For example, a serviceprovider may seek to improve a technical service or user interface butmay lack or be denied access to sufficient data to implement analysisand change. As one example, a payment processor may be driven to provideworld-class online payment services but may only be allowed atransactional presence by an online merchant as the very last step in apurchase transaction funnel. In typical arrangements, the very last pageis a checkout page, and data can generally only be sourced from thispage.

Thus, the requisite data that might otherwise allow an informed analysisof a user experience is limited in such conventional arrangements. Thistechnical shortcoming can present a significant challenge to thecontinued development of online payment services or other technicalservices.

Moreover, although a customer may ultimately make a purchase, not allpurchases are made with a positive mindset. That is, a customer may makea purchase but nevertheless be frustrated, upset, or uncertain about thepurchase just made. A present technical inability to detect a customermindset based on data sourced from a checkout page can result incompromised transactional results and system inefficiency. A comprisedsystem can lead to a significant waste of technical resources andtechnical inefficiency. Other drawbacks can include increases inpurchase returns, chargebacks, loss of future or recurring business, andthe like.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating a high-level networkarchitecture, according to an example embodiment.

FIG. 2 is a block diagram showing architectural aspects of a publicationsystem, according to some example embodiments.

FIG. 3 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 4 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

FIG. 5 is a block diagram showing aspects of an online method forconducting a transaction between a merchant site and an electronic userdevice using a payment processor, according to an example embodiment.

FIG. 6 is a block diagram illustrating an example payment processor, inaccordance with an example embodiment.

FIG. 7 is a flow diagram illustrating a method, in accordance with anexample embodiment.

DETAILED DESCRIPTION

"Carrier Signal" in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bya machine, and includes digital or analog communication signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device and using any one ofa number of well-known transfer protocols.

"Client Device" or "Electronic Device" in this context refers to anymachine that interfaces to a communications network to obtain resourcesfrom one or more server systems or other client devices. A client devicemay be, but is not limited to, a mobile phone, desktop computer, laptop,portable digital assistant (PDA), smart phone, tablet, ultra-book,netbook, laptop, multiprocessor system, microprocessor-based orprogrammable consumer electronic system, game console, set-top box, orany other communication device that a user may use to access a network.

"Customer's Electronic Device" or "Electronic User Device" in thiscontext refers to a client device that a customer uses to interact witha merchant. Examples of this device include a desktop computer, a laptopcomputer, a mobile device (e.g., smart phone, tablet), and a gameconsole. The customer's electronic device may interact with the merchantvia a browser application that executes on the customer's electronicdevice or via a native app installed onto the customer's electronicdevice. The client-side application executes on the customer'selectronic device.

"Communications Network" in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network, andcoupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or anothertype of cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1xRTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long-Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

"Component" in this context refers to a device, physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, application programming interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components.

A "hardware component" is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various example embodiments, one or more computer systems(e.g., a standalone computer system, a client computer system, or aserver computer system) or one or more hardware components of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein. A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors.

It will be appreciated that the decision to implement a hardwarecomponent mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase "hardware component" (or "hardware-implemented component")should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instant in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instant of time and to constitute adifferent hardware component at a different instant of time. Hardwarecomponents can provide information to, and receive information from,other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, "processor-implemented component"refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a"cloud computing" environment or as a "software as a service" (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

"Machine-Readable Medium" in this context refers to a component, device,or other tangible medium able to store instructions and data temporarilyor permanently and may include, but not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EPROM)), and/or anysuitable combination thereof. The term "machine-readable medium" shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term "machine-readable medium" shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a "machine-readablemedium" refers to a single storage apparatus or device, as well as"cloud-based" storage systems or storage networks that include multiplestorage apparatus or devices. The term "machine-readable medium"excludes signals per se.

"Processor" in one context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,"commands," "op codes," "machine code," etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an ASIC, a RadioFrequencyIntegrated Circuit (RFIC), or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as "cores") that may executeinstructions contemporaneously.

In another context, a "Processor" (e.g., a processor 540 in FIG. 5 ) isa company (often a third party) appointed to handle payment card (e.g.,credit card, debit card) transactions. They have connections to variouscard networks and supply authorization and settlement services tomerchants or payment service providers. In aspects, they can also movethe money from an issuing bank to a merchant or acquiring bank.

"Card Network" (or "Card Association") in this context refers tofinancial payment networks such as Visa®, MasterCard®, AmericanExpress®, Diners Club®, JCB®, and China Union-Pay®.

"Acquiring Bank" or "Acquirer" in this context refers to a bank orfinancial institution that accepts credit and/or debit card paymentsfrom affiliated card networks for products or services on behalf of amerchant or payment service provider.

"Card Issuing Bank" or "Issuing Bank" in this context refers to a bankthat offers card network or association-branded payment cards directlyto consumers. An issuing bank assumes primary liability for theconsumer's capacity to pay off debts they incur with their card.

"Payment Information" includes information generally required tocomplete a transaction, and the specific type of information providedmay vary by payment type. Some payment information will be sensitive(e.g., the card validation code), while other information might not be(e.g., a zip code). For example, when a payment is made via a creditcard or debit card, the payment information includes a primary accountnumber (PAN) or credit card number, card validation code, and expirationmonth and year. In another payment example, made using an AutomatedClearinghouse (ACH) transaction for example, the payment informationincludes a bank routing number and an account number within that bank.

"Sensitive information" may not necessarily be related to paymentinformation and may include other confidential personal information,such as medical (e.g., HIPAA) information, for example. The ambit of theterm "Payment Information" includes "Sensitive Information" within itsscope. In some examples, sensitive payment information may include"regulated payment information," which may change over time. Forexample, currently a merchant cannot collect more than the first six (6)or the last four (4) numbers of a customer's PAN without generallyneeding to comply with Payment Card Industry (PCI) regulations. But cardnumber lengths may change, and when they do, the "6 and 4" rules willlikely change with them. These potential future changes are incorporatedwithin the ambit of "regulated payment information," which is, in turn,included within the ambit of the term "payment information" as definedherein.

"Merchant" in this context refers to an entity that is associated withselling or licensing products and/or services over electronic systemssuch as the Internet and other computer networks. The merchant may bethe direct seller/licensor, or the merchant may be an agent for a directseller/licensor. For example, entities such as Amazon® sometimes act asthe direct seller/licensor, and sometimes act as an agent for a directseller/licensor.

"Merchant Site" in this context refers to an e-commerce site or portal(e.g., website, or mobile app) of the merchant. In some embodiments, themerchant (e.g., a merchant 510 of FIG. 5 ) and merchant servers (e.g.,merchant servers 512 of FIG. 5 ) are associated with the merchant site.The merchant site is associated with a client-side application and aserver-side application. In one example embodiment, the merchant siteincludes the merchant servers 512 of FIG. 5 , and the server-sideapplication executes on the merchant servers 512.

"Payment Processor" in this context (e.g., a payment processor 530 inFIG. 5 ) refers to an entity or a plurality of entities that facilitatea transaction, for example between a merchant and a customer'selectronic device. With reference to a high-level descriptionillustrated in FIG. 5 , in some examples described more fully below, thepayment processor includes selected functionality of both the paymentprocessor 530 and the processor 540/card networks 550. For example, thepayment processor 530 creates tokens and maintains and verifiespublishable (non-secret) keys and secret keys. In the illustratedexample, the processor 540/card networks 550 are involved in authorizingor validating payment information. In one example embodiment, thepayment processor 530 and the processor 540/card networks 550 functiontogether to authorize and validate payment information, issue a token,and settle any charges that are made. Accordingly, in this embodiment,"payment processor" refers to the functionality of the payment processor530 and the functionality of the processor 540/card networks 550. Inanother example embodiment, wherein step (3) in the high-leveldescription is not performed, and the payment processor 530 performs itsown verification before issuing a token, the processor 540/card networks550 are still used for settling any charges that are made, as describedin step (7). Accordingly, in this embodiment, "payment processor" mayrefer only to the functionality of the payment processor 530 withrespect to issuing tokens. Further, in the example arrangement shown,the payment processor 530, the processor 540, and the card networks 550are shown as separate entities. In some examples, their respectivefunctions may be performed by two entities, or even just one entity,with the entities themselves being configured accordingly.

"Native Application" or "native app" in this context refers to an appcommonly used with a mobile device, such as a smart phone or tablet.When used with a mobile device, the native app is installed directlyonto the mobile device. Mobile device users typically obtain these appsthrough an online store or marketplace, such as an app store (e.g.,Apple's App Store, Google Play store). More generically, a nativeapplication is designed to run in the computer environment (machinelanguage and operating system) that it is being run in. It can bereferred to as a "locally installed application." A native applicationdiffers from an interpreted application, such as a Java applet, whichmay require interpreter software. A native application also differs froman emulated application that is written for a different platform andconverted in real time to run, and a web application that is run withinthe browser.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2011-2018, Stripe, Inc., All Rights Reserved.

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

With reference to FIG. 1 , an example embodiment of a high-level SaaSnetwork architecture 100 is shown. A networked system 116 providesserver-side functionality via a network 110 (e.g., the Internet or aWAN) to a client device 108. A web client 102 and a programmatic client,in the example form of a client application 104, are hosted and executeon the client device 108. The networked system 116 includes anapplication server 122, which in turn hosts a publication system 106(such as the publication system hosted at https://stripe.com by Stripe,Inc. of San Francisco, CA (herein "Stripe") as an example of a paymentprocessor 530) that provides a number of functions and services to theclient application 104 that accesses the networked system 116. Theclient application 104 also provides a number of interfaces describedherein, which can present an output in accordance with the methodsdescribed herein to a user of the client device 108.

The client device 108 enables a user to access and interact with thenetworked system 116 and, ultimately, the publication system 106. Forinstance, the user provides input (e.g., touch screen input oralphanumeric input) to the client device 108, and the input iscommunicated to the networked system 116 via the network 110. In thisinstance, the networked system 116, in response to receiving the inputfrom the user, communicates information back to the client device 108via the network 110 to be presented to the user.

An API server 118 and a web server 120 are coupled, and provideprogrammatic and web interfaces respectively, to the application server122. The application server 122 hosts the publication system 106, whichincludes components or applications described further below. Theapplication server 122 is, in turn, shown to be coupled to a databaseserver 124 that facilitates access to information storage repositories(e.g., a database 126). In an example embodiment, the database 126includes storage devices that store information accessed and generatedby the publication system 106.

Additionally, a third-party application 114, executing on one or morethird-party servers 112, is shown as having programmatic access to thenetworked system 116 via the programmatic interface provided by the APIserver 118. For example, the third-party application 114, usinginformation retrieved from the networked system 116, may support one ormore features or functions on a website hosted by a third party.

Turning now specifically to the applications hosted by the client device108, the web client 102 may access the various systems (e.g., thepublication system 106) via the web interface supported by the webserver 120. Similarly, the client application 104 (e.g., an "app" suchas a payment processor app) accesses the various services and functionsprovided by the publication system 106 via the programmatic interfaceprovided by the API server 118. The client application 104 may be, forexample, an "app" executing on the client device 108, such as an iOS orAndroid OS application to enable a user to access and input data on thenetworked system 116 in an offline manner and to perform batch-modecommunications between the client application 104 and the networkedsystem 116.

Further, while the SaaS network architecture 100 shown in FIG. 1 employsa client-server architecture, the present inventive subject matter is ofcourse not limited to such an architecture, and could equally well findapplication in a distributed, or peer-to-peer, architecture system, forexample. The publication system 106 could also be implemented as astandalone software program, which does not necessarily have networkingcapabilities.

FIG. 2 is a block diagram showing architectural details of a publicationsystem 106, according to some example embodiments. Specifically, thepublication system 106 is shown to include an interface component 210 bywhich the publication system 106 communicates (e.g., over a network 110)with other systems within the SaaS network architecture 100.

The interface component 210 is communicatively coupled to a paymentprocessor component 300 that operates to provide payment processingfunctions for a payment processor (e.g., a payment processor 530, FIG. 5) in accordance with the methods described herein with reference to theaccompanying drawings.

FIG. 3 is a block diagram illustrating an example software architecture306, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 3 is a non-limiting example of asoftware architecture 306, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 306 may execute on hardwaresuch as a machine 400 of FIG. 4 that includes, among other things,processors 404, memory/storage 406, and input/output (I/O) components418. A representative hardware layer 352 is illustrated and canrepresent, for example, the machine 400 of FIG. 4 . The representativehardware layer 352 includes a processor 354 having associated executableinstructions 304. The executable instructions 304 represent theexecutable instructions of the software architecture 306, includingimplementation of the methods, components, and so forth describedherein. The hardware layer 352 also includes memory and/or storagemodules as memory/storage 356, which also have the executableinstructions 304. The hardware layer 352 may also comprise otherhardware 358.

In the example architecture of FIG. 3 , the software architecture 306may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 306 mayinclude layers such as an operating system 302, libraries 320,frameworks/middleware 318, applications 316, and a presentation layer314. Operationally, the applications 316 and/or other components withinthe layers may invoke API calls 308 through the software stack andreceive a response as messages 312 in response to the API calls 308. Thelayers illustrated are representative in nature, and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 318, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 302 may manage hardware resources and providecommon services. The operating system 302 may include, for example, akernel 322, services 324, and drivers 326. The kernel 322 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 322 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 324 may provideother common services for the other software layers. The drivers 326 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 326 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 320 provide a common infrastructure that is used by theapplications 316 and/or other components and/or layers. The libraries320 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 302 functionality (e.g., kernel 322,services 324, and/or drivers 326). The libraries 320 may include systemlibraries 344 (e.g., C standard library) that may provide functions suchas memory allocation functions, string manipulation functions,mathematical functions, and the like. In addition, the libraries 320 mayinclude API libraries 346 such as media libraries (e.g., libraries tosupport presentation and manipulation of various media formats such asMPEG4, H.264, MP3, AAC, AMR, JPG, and PNG), graphics libraries (e.g., anOpenGL framework that may be used to render 2D and 3D graphic content ona display), database libraries (e.g., SQLite that may provide variousrelational database functions), web libraries (e.g., WebKit that mayprovide web browsing functionality), and the like. The libraries 320 mayalso include a wide variety of other libraries 348 to provide many otherAPIs to the applications 316 and other software components/modules.

The frameworks/middleware 318 provide a higher-level commoninfrastructure that may be used by the applications 316 and/or othersoftware components/modules. For example, the frameworks/middleware 318may provide various graphic user interface (GUI) functions 342,high-level resource management, high-level location services, and soforth. The frameworks/middleware 318 may provide a broad spectrum ofother APIs that may be utilized by the applications 316 and/or othersoftware components/modules, some of which may be specific to aparticular operating system or platform.

The applications 316 include built-in applications 338 and/orthird-party applications 340. Examples of representative built-inapplications 338 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 340 may includeany application developed using the ANDROID™ or IOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform and may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobileoperating systems. The third-party applications 340 may invoke the APIcalls 308 provided by the mobile operating system (such as the operatingsystem 302) to facilitate functionality described herein.

The applications 316 may use built-in operating system functions (e.g.,kernel 322, services 324, and/or drivers 326), libraries 320, andframeworks/middleware 318 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 314. In these systems, the application/component"logic" can be separated from the aspects of the application/componentthat interact with a user.

Some software architectures use virtual machines. In the example of FIG.3 , this is illustrated by a virtual machine 310. The virtual machine310 creates a software environment where applications/components canexecute as if they were executing on a hardware machine (such as themachine 400 of FIG. 4 , for example). The virtual machine 310 is hostedby a host operating system (e.g., the operating system 302 in FIG. 3 )and typically, although not always, has a virtual machine monitor 360,which manages the operation of the virtual machine 310 as well as theinterface with the host operating system (e.g., the operating system302). A software architecture executes within the virtual machine 310such as an operating system (OS) 336, libraries 334, frameworks 332,applications 330, and/or a presentation layer 328. These layers ofsoftware architecture executing within the virtual machine 310 can bethe same as corresponding layers previously described or may bedifferent.

FIG. 4 is a block diagram illustrating components of a machine 400,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 4 shows a diagrammatic representation of the machine400 in the example form of a computer system, within which instructions410 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 400 to perform any one ormore of the methodologies discussed herein may be executed. As such, theinstructions 410 may be used to implement modules or componentsdescribed herein. The instructions 410 transform the general,non-programmed machine 400 into a particular machine 400 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 400 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 400 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 400 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 410, sequentially or otherwise, that specify actions to betaken by the machine 400. Further, while only a single machine 400 isillustrated, the term "machine" shall also be taken to include acollection of machines that individually or jointly execute theinstructions 410 to perform any one or more of the methodologiesdiscussed herein.

The machine 400 may include processors 404 (including processors 408 and412), memory/storage 406, and I/O components 418, which may beconfigured to communicate with each other such as via a bus 402. Thememory/storage 406 may include a memory 414, such as a main memory, orother memory storage, and a storage unit 416, both accessible to theprocessors 404 such as via the bus 402. The storage unit 416 and memory414 store the instructions 410 embodying any one or more of themethodologies or functions described herein. The instructions 410 mayalso reside, completely or partially, within the memory 414, within thestorage unit 416, within at least one of the processors 404 (e.g.,within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 400. Accordingly, thememory 414, the storage unit 416, and the memory of the processors 404are examples of machine-readable media.

The I/O components 418 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 418 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 418may include many other components that are not shown in FIG. 4 . The I/Ocomponents 418 are grouped according to functionality merely forsimplifying the following discussion, and the grouping is in no waylimiting. In various example embodiments, the I/O components 418 mayinclude output components 426 and input components 428. The outputcomponents 426 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 428 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 418 may includebiometric components 430, motion components 434, environment components436, or position components 438, among a wide array of other components.For example, the biometric components 430 may include components todetect expressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 434 may include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environment components436 may include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas sensors to detect concentrationsof hazardous gases for safety or to measure pollutants in theatmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 438 may include location sensorcomponents (e.g., a Global Positioning System (GPS) receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 418 may include communication components 440 operableto couple the machine 400 to a network 432 or devices 420 via a coupling424 and a coupling 422, respectively. For example, the communicationcomponents 440 may include a network interface component or othersuitable device to interface with the network 432. In further examples,the communication components 440 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 420 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 440 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 440 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components440, such as location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

In some embodiments, a JavaScript library can be embedded into amerchant's checkout form to handle credit card information. When a userattempts to complete a transaction using the checkout form, it sends thecredit card information directly from the user's browser to the paymentprocessor's servers. The JavaScript library provides merchants with aset of technologies that can be easily and quickly integrated tosecurely accept payments online. With the JavaScript library, merchantsretain full control of their customers' payment flows, but their serversare never exposed to sensitive payment information.

When added to a merchant's payment form, the JavaScript libraryautomatically intercepts the payment form submission, sending paymentinformation directly to the payment processor and converting it to asingle-use token. The single-use token can be safely passed to themerchant's systems and used later to charge customers. Merchants havecomplete control of their customers' payment experience without everhandling, processing, or storing sensitive payment information.

Viewed generally in one example, and with reference to FIG. 5 , apayment processing flow is now described:

1. The merchant's customer 520 uses an Internet-enabled browser 521 tovisit the merchant's site. The customer 520 is served a JavaScriptlibrary-enabled payment form 511 using standard web technologies. Thecustomer 520 enters the specified information including their paymentinformation 522 and submits the payment form 511. The billing infoportion of the payment form 511 is for payment via a credit card ordebit card. If payment is to be made via an ACH transaction, the billinginfo portion of the payment form 511 will request a bank routing numberand an account number within that bank, and possibly additionalinformation, such as the bank name and whether the account is a checkingor savings account.

2. The customer's payment information 522 is sent from the customer'sbrowser 521 to the payment processor 530, never touching the merchantservers 512. In this manner, the client-side application electronicallysends payment information retrieved from the customer's electronicdevice to the payment processor 530. The client-side application doesnot send the payment information 522 to the server-side application.

3. In one preferred embodiment, the payment processor 530 submits therelevant transaction to a processor 540 or directly to the card network550 for authorization or validation of the payment information. The cardnetwork 550 sends the request to the card issuing bank 560, whichauthorizes the transaction. In this embodiment, the payment processor530 and the processor 540/card network 550 function together as apayment processor. In another example embodiment, this step is performedwithout any communication to the processor 540/card network 550.Instead, the payment processor 530 performs its own authorization orvalidation of the payment information using heuristic means, such as bychecking the Bank Identification Number (BIN), also referred to as theIssuer Identification Number (IIN), against a database of known, validBINs on file with the payment processor 530. (The BIN is a part of thebank card number, namely the first six digits.) In yet another exampleembodiment, this step is not performed at all since the authorization orvalidation is not necessary for the next step (4) to succeed. That is,it is acceptable to create a single-use token in step (4) thatrepresents payment information which has not been validated in any way.

4. If authorized, the payment processor 530 will generate and return asecure, single-use token 535 to the customer's browser 521 thatrepresents the customer's payment information but does not leak anysensitive information. In the example embodiment wherein step (3) is notperformed, the payment processor 530 performs this step without waitingto receive authorization from the processor 540 or the card network 550.In this manner, the payment processor 530 creates the token 535 from thepayment information sent by the client-side application, wherein thetoken 535 functions as a proxy for the payment information 522.

5. The payment form 511 is submitted to the merchant servers 512,including the single-use token 535. More specifically, the paymentprocessor 530 sends the token 535 to the client-side application, which,in turn, sends the token 535 to the server-side application for use bythe server-side application in conducting the transaction.

6. The merchant 510 uses the single-use token 535 to submit a chargerequest to the payment processor 530 (or to create a customer object forlater use). In this step, the payment processor 530 submits a request toauthorize the charge to the processor 540 or directly to the cardnetwork 550. This authorization specifies the actual amount to chargethe credit card. If an authorization was already done in step (3) forthe correct amount, this authorization request can be skipped. This maybe a one-time payment for a merchant item, or it may involve registeringthe payment information with the merchant site for subsequent use inmaking a payment for a merchant item (a so-called "card on file"scenario). Using the process described in steps (1) through (6), thepayment information can be used by the server-side application via thetoken 535 without the server-side application being exposed to thepayment information.

7. The payment processor 530 settles the charge on behalf of themerchant 510 with the processor 540 or directly with the card network550.

8. The card network 550 causes the funds to be paid by the card issuingbank 560 to the payment processor 530 or to the payment processor'sacquiring bank 570.

9. The payment processor 530 causes the settled funds to be sent to themerchant 510 (or to the merchant's bank 580), net of any applicablefees.

10. The card issuing bank 560 collects the paid funds from the customer520.

Not all of the steps listed above need happen in real time. Otherexamples, arrangements, and functionality are possible. Applicant'spublished pat. application U.S. 2013/0117185 A1 is incorporated byreference in its entirety in this regard. Typically, when the merchant'scustomer submits the payment form in step (1), steps (1) through (6)happen in real time and steps (7) through (10) happen later, usuallyonce per day, as a batch process settling all of the funds for all ofthe payment processor's merchants. In some examples, the paymentprocessor uses an HTTP-based tokenization API in steps (2) and (4)above. Some broader examples may be considered as "tokenization as aservice," in which any data is tokenized. One general example mayfacilitate a merger and acquisition (M&A) analysis in which companieswant to compare an overlap in their customer bases. A payment processor(acting as a tokenization service) can tokenize the customers of eachcompany and compare the overlap without revealing confidentialinformation to either party. Unique payment tokens can be adapted toenable and facilitate such a tokenization service.

Aspects of the present disclosure use technical signals observable fromthe checkout page of a purchase transaction funnel to generate new datarepresentative of inferences about a customer's experience in making aparticular purchase. In some examples, the technical signals areobservable exclusively from, or can only be extracted from, a checkoutpage of a purchase transaction funnel. In some examples, the data isextracted from novel sources and includes data previously denied toservice providers as being too difficult or cumbersome to extract. Theextracted new data can be structured and presented in novel ways toprovide feedback to a merchant, for example, to improve technicalservices such as a checkout flow or payment experience. As describedfurther above, conventional transactional systems have been blind tothis data, or lacked the technology to access it.

FIG. 6 is a block diagram illustrating an example payment processor 530,in accordance with an example embodiment. Here, the example paymentprocessor 530 includes a transactional behavior model 604. Thetransactional behavior model 604 acts to process and reprocess paymentsmade through a checkout page in a purchase transaction funnel. Inembodiments, the transactional behavior model 604 is trained based uponsignals observed from the checkout page alone to predict, for example, abehavior of a purchaser (also termed a "customer" or "user" herein).Example predicted behaviors may include an action taken pursuant to acheckout user being dissatisfied with an aspect of a purchasetransaction. Other predicted behaviors are possible, for example, anaction taken pursuant to a user being satisfied with an aspect of thepurchase transaction.

In some embodiments, a predicted behavior includes a user seeking areturn or chargeback for a given purchase transaction. In some examples,a trained transactional behavior model 604 is built using historicalmerchant transaction data to predict, for a particular merchant forexample, whether new transactions are likely to be satisfactory or not,as measured for example by whether a customer takes a negative useraction such as seeking a refund, makes a chargeback in relation to apurchase transaction, or returns as a customer to the merchant. Othernegative user actions are possible.

The transactional behavior model 604 may be trained via a machinelearning algorithm 606. Specifically, training data may be obtained froma data source (not pictured) such as a checkout page in a purchasetransaction funnel. In some example embodiments, the training data isinitially stored in a Hadoop cluster and comprises information onprevious purchase transactions from various customers and merchants. Tothe extent that such information is available, this training data canalso include information on the customers and merchants themselves, suchas, for example, their respective locations, merchant classifications,previous payment history, and the like.

In an example embodiment, the training data may comprise onlyinformation from payment attempts that result in a negative user actionsuch as customer refunds, chargebacks, or returns. In a further exampleembodiment, each piece of training data may contain two sections in aspecific data structure: a first customer (or user) interaction sectionand a second checkout page data section, as well as a label indicatingwhether a given purchase transaction was successful or not, based on oneor more criteria.

In some examples, the first customer interaction section contains datarelating to any interaction attribute relevant to the conclusion of asuccessful or unsuccessful purchase transaction. Example interactionattributes may include those listed below:

Interaction Attribute Table Attribute 1 A time period between theloading of a checkout page and the taking or completion of a paymentaction. A payment action may include one or more of a completion or partcompletion of a payment form presented in the checkout page, completinga transaction, making a payment submission, and making a submission of apayment page Attribute 2 A number of times a customer viewed a checkoutpage before taking or completing a payment action Attribute 3 A numberof typos or other field mis-entries requiring correction prior to takingor completing a payment action Attribute 4 A detection of whether thecustomer omitted any required fields as part of his or her initialpurchase attempt that needed to be corrected prior to taking orcompleting a payment action Attribute 5 A number, type, and frequency ofmouse movements Attribute 6 A detection of whether a payment instrumentwas declined Attribute 7 A detection of whether a customer switched to adifferent payment instrument to complete the transaction Attribute 8 Apurchase transaction location (generally, from reverse IP lookup) and,relatedly, a local time at which the purchase transaction occurred

In some examples, the checkout page data section contains any checkoutpage attribute relevant to the conclusion of a successful orunsuccessful purchase transaction. Example checkout page attributes mayinclude those listed below:

Checkout Page Attributes Attribute 1 A detection of whether a customeris a first-time customer or repeat customer. (An inference may be drawnon the basis that repeat customers are more likely to be satisfied witha given merchant user, while this does not necessarily follow for newcustomers.) Attribute 2 If a repeat customer is detected, a time periodelapsed since a prior transaction relative to a customer average. (Aninference may be drawn on the basis of perceived customer reliabilityrelative to other existing customers.) Attribute 3 A basket size of thetransaction relative to a customer's average basket size. (An inferenceregarding customer sentiment may be drawn based on whether this is anatypically sized order for this type of merchant.) Attribute 4 Apurchase time for a location relative to a customer's average purchasetime in that location. (An inference regarding customer sentiment orconvenience may be drawn based on whether this purchase is made at anatypical time of day for this location or type of good.) Attribute 5 Apayment instrument (e.g., a credit card) locality and currency to givean indication of a customer's location relative to the merchant.Attribute 6 Information from customer orders or shipping API data. (Forexample, a checkout cart containing multiple sizes of the same clothingproduct may indicate that the customer is insecure about a product fitand more likely to return all but one of them. A selected speed ofshipping, for example next-day FedEx vs. USPS, may also be indicative ofcustomer agitation or an expected dissatisfaction with an aspect of apurchase transaction.) Attribute 7 A detection of whether a purchasetransaction resulted in a chargeback. (Chargebacks generally includemore embedded metadata, and a type of chargeback can be determined.Chargebacks are an extremely strong signal that a customer was unhappywith an aspect of a purchase transaction.) Attribute 8 A detection ofwhether a purchase transaction resulted in a refund. (A refund is anextremely strong signal that a customer was unhappy with an aspect of apurchase transaction.) Attribute 9 A detection of whether a purchasetransaction resulted in a subsequent support ticket or request. (Asupport ticket can signal that a customer was unhappy with an aspect ofa purchase transaction.) Attribute 10 Customer review data.

A training data preprocessing component 608 may preprocess the trainingdata, including, for example, applying a MapReduce function or similarfunctionality to the training data. A feature extraction component 610may then act to extract a plurality of features from the preprocessedtraining data and feed these features into the machine learningalgorithm 606. The extracted features may relate to one or more of theinferences discussed above. The machine learning algorithm 606 learnsweights assigned to each of the features and applies these weights to afunction. The function and the learned weights comprise themachine-learned transactional behavior model 604, which may be stored ina file system 612 and retrieved when needed to perform analysis of acandidate purchase transaction at various potential transaction times orunder various purchase transaction conditions.

The machine learning algorithm 606 may be selected from among manydifferent potential supervised or unsupervised machine learningalgorithms. Examples of supervised machine learning algorithms includeartificial neural networks, Bayesian networks, instance-based learning,support vector machines, random forests, linear classifiers, quadraticclassifiers, k-nearest neighbor, decision trees, and hidden Markovmodels. Examples of unsupervised machine learning algorithms includeexpectation-maximization algorithms, vector quantization, andinformation bottleneck methods. In an example embodiment, a binarylogistic regression model is used. Binary logistic regression deals withsituations in which the observed outcome for a dependent variable canhave only two possible types. Logistic regression is used to predict theodds of one case or the other being true based on values of independentvariables (predictors). In a further example embodiment, a boosted treegradient descent process is utilized for the machine learning.

The function contained in the transactional behavior model 604 may beevaluated at runtime to produce a transactional behavior score. Thetransactional behavior score is a prediction of the likelihood that anattempted purchase transaction will result in a successful (orunsuccessful) payment (i.e., invoke a positive user action) or invoke anegative user action, based on evaluating various features and applyingthe feature weights learned by the machine learning algorithm 606 to thefeatures. In some examples, a predicted transaction may include a hybridresult, for example result in both a successful payment and a negativeuser action (e.g., a complaint, or a bad review, and so forth).

In other aspects, transactional behavior scores can be input as datainto an optimization function 605 to dynamically optimize or change acheckout page (user interface) to maximize or at least increase aprobability of purchase transaction success, i.e., positive user action.Said another way, the checkout page is dynamically optimized to reducethe probability of a negative user action in relation to a givenpurchase transaction. In an example embodiment, the transactionalbehavior score is optimized at evaluation time in accordance with theoptimization function 605. This optimization function 605 acts tooptimize the transactional behavior score such that the output of thetransactional behavior model 604 not only maximizes, or at leastincreases, a likelihood that a purchase transaction will be processedsuccessfully, but also minimizes, or at least reduces, a likelihood ofcustomer dissatisfaction, or negative user action.

In some examples, a predicted customer sentiment may be determineddynamically at checkout. That is, the predicted sentiment or behavior ofcustomers over a period of time (for example, a day or a week) can beused in some examples as a fitness function for agenetic-algorithm-based optimizer (for example, the optimizationfunction 605) to dynamically change the type, layout, or other aspectsof a checkout page presented in a user interface to increase aprobability of transaction success and assess a predicted sentiment orbehavior of customers making transactions.

In still other practical aspects, the improved technology allows datarepresenting predicted user behavior or sentiment based on customertransactions to be used as an input to upsell or provide offers tocustomers. In other practical examples, customers who have completedtransactions but nevertheless were predicted to be unsatisfied can betargeted specifically with offers, bonuses, or other incentives as a wayto reduce the likelihood of a refund, chargeback, or bad net promoterscore (NPS). In still other aspects, a merchant can be provided withinformation about their average customer transactional behavior scorerelative to an anonymized set of peer companies, such that the merchantcan choose to make changes to their checkout experience to increasecustomer transaction satisfaction.

In each of the embodiments described herein, a client device 108 may beconfigured, or caused to be configured, by a payment processor 530, oran operating system such as iOS or Android, to execute the operations orprovide the checkout page functionality described herein.

Thus, in some embodiments, a system for processing electronic paymentsis provided. An example system may comprise a network; one or morehardware processors; and a memory storing instructions that, whenexecuted by at least one processor among the processors, cause thescheduling system to perform operations comprising, at least: obtainingtraining data from a data source, the training data relating to priorpurchases made via the electronic payment processing system, the datasource including a checkout page in a purchase transaction funnel;extracting one or more features from the training data relating to theprior purchases, the one or more features associated with a negativeuser action invoked in relation to at least one of the prior purchases;for each of a plurality of real-time purchases, feeding the one or moreextracted features into a transactional behavior model, thetransactional behavior model trained via a machine learning algorithm toproduce a transactional behavior score indicative of a probability thata first real-time purchase among the plurality of real-time purchaseswill invoke a negative user action taken in relation to the firstreal-time purchase; using one or more transactional behavior scores forthe plurality of real-time purchases to predict respective occurrencesof invoked negative user action; and based on the predicted occurrences,dynamically optimizing the checkout page in the purchase transactionfunnel to reduce a probability that a second real-time purchase amongthe plurality of real-time purchases will invoke a negative user actiontaken in relation to the second real-time purchase.

Embodiments of the present disclosure include methods. FIG. 7 is a flowdiagram illustrating an example method 700, for processing payments madevia an electronic payment processing system in accordance with anexample embodiment. The method 700 includes, at operation 702, obtainingtraining data from a data source, the training data relating to priorpurchases made via the electronic payment processing system, the datasource including a checkout page in a purchase transaction funnel; atoperation 704, extracting one or more features from the training datarelating to the prior purchases, the one or more features associatedwith a negative user action invoked in relation to at least one of theprior purchases; at operation 706, for each of a plurality of real-timepurchases, feeding the one or more extracted features into atransactional behavior model, the transactional behavior model trainedvia a machine learning algorithm to produce a transactional behaviorscore indicative of a probability that a first real-time purchase amongthe plurality of real-time purchases will invoke a negative user actiontaken in relation to the first real-time purchase; at operation 708,using one or more transactional behavior scores for the plurality ofreal-time purchases to predict respective occurrences of invokednegative user action; and, at operation 710, based on the predictedoccurrences, dynamically optimizing the checkout page in the purchasetransaction funnel to reduce a probability that a second real-timepurchase among the plurality of real-time purchases will invoke anegative user action taken in relation to the second real-time purchase.

In some examples, the data source is confined to data contained in orassociated with the checkout page in the purchase transaction funnel.

In some examples, the invoked negative user action includes one or moreof a refund request, a chargeback request, and a return request.

In some examples, the operations further comprise confining the trainingdata to information extracted from the checkout page and associated withthe invoked negative user action.

In some examples, the training data includes a data structure, the datastructure including a first user interaction section and a secondcheckout page data section.

In some examples, the first user interaction section includes datasourced exclusively from the checkout page and relating to one or moreof: a time period between a loading of the checkout page and a taking orcompletion of a payment action; a number of times or frequency acustomer viewed the checkout page before taking or completing a paymentaction; a number of typos or other mis-entries corrected prior to takingor completing a payment action; a detection of an omitted fieldcompleted prior to taking or completing a payment action; a number,type, or frequency of a mouse movement; a detection of a payment denial;a detection of a decline of a payment instrument; a detection of asubstitution of the payment instrument; an IP address associated with aprior purchase, the first real-time purchase, or the second real-timepurchase; and a local user time of the prior purchase, the firstreal-time purchase, or the second real-time purchase.

In some examples, the second checkout page data section includes datasourced exclusively from the checkout page and relating to one or moreof: a detection of a first-time customer; a detection of a repeatcustomer; a time period of an interval between a prior purchase and thefirst real-time or second real-time purchase, assessed relative to acustomer average for the same interval; a user cart size associated withthe prior purchase and the first real-time or second real-time purchase,assessed relative to an average user cart size for a respectivepurchase; a locality or default currency of a payment instrumentassessed relative to a locality or currency of a merchant store orwebsite; user order or shipping API data; a selection of a shippingservice or shipping rate; a degree of embedded metadata associated witha user action; a detection of a refund; a detection of a support ticketor request; and user review data.

Some embodiments include machine-readable media including instructionswhich, when read by a machine, cause the machine to perform theoperations of any one or more of the methodologies summarized above, ordescribed elsewhere herein.

Although the subject matter has been described with reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the disclosed subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show by way of illustration, and not oflimitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by any appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term "invention" merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A method of processing payments made via anelectronic payment processing system, the method comprising: obtainingtraining data relating to prior purchase transactions processed via theelectronic payment processing system, the training data including dataderived from respective checkout pages presented by respectiveinterfaces of the electronic payment processing system in respectivecheckout flows of the prior purchase transactions; extracting one ormore features from the training data, the one or more featuresassociated with a negative user action invoked in relation to at leastone of the prior purchase transactions; for a real-time purchasetransaction not included in the training data, using the extracted oneor more features associated with the negative user action to derive atransactional behavior score indicative of a probability that thereal-time purchase transaction will invoke the negative user actiontaken by a user in relation to the real-time purchase transaction; basedon the transactional behavior score, causing a dynamic optimization,during a respective checkout flow of the real-time purchase transaction,of a user interface presenting a checkout page in the respectivecheckout flow, the dynamic optimization of the user interface includinginsertion in the respective checkout flow of a targeted remedial actionto reduce a probability that the real-time purchase transaction willinvoke the negative user action taken in relation to the real-timepurchase transaction; receiving a request to invoke the targetedremedial action; and in response to receiving the request to invoke thetargeted remedial action, triggering the targeted remedial action. 2.The method of claim 1, wherein the training data is obtained from a datasource, the data source confined to data contained in or associated withthe respective checkout pages.
 3. The method of claim 2, wherein theinvoked negative user action includes one or more of a refund request, achargeback request, and a return request.
 4. The method of claim 3,further comprising confining the training data to information extractedfrom the respective checkout pages and associated with the invokednegative user action.
 5. The method of claim 1, wherein the trainingdata includes a data structure, the data structure including a firstuser interaction section and a checkout page data section.
 6. The methodof claim 5, wherein the first user interaction section includes datarelating to one or more of: a time period between a loading of thecheckout page and a taking or completion of a payment action; a numberof times or frequency a customer viewed the checkout page before takingor completing a payment action; a number of typos or other mis-entriescorrected prior to taking or completing a payment action; a detection ofan omitted field completed prior to taking or completing a paymentaction; a number, type, or frequency of a mouse movement; a detection ofa payment denial; a detection of a decline of a payment instrument; adetection of a substitution of the payment instrument; an IP addressassociated with a prior purchase or the real-time purchase; and a localuser time of the prior purchase or the real-time purchase.
 7. The methodof claim 5, wherein the checkout page data section includes datarelating to one or more of: a detection of a first-time customer; adetection of a repeat customer; a time period of an interval between aprior purchase and the real-time purchase, assessed relative to acustomer average for the interval; a user cart size associated with thereal-time purchase, assessed relative to an average user cart size for apurchase; a locality or default currency of a payment instrument to givean indication of a customer's location relative to a merchant; userorder or shipping API data; a selection of a shipping service orshipping rate; embedded metadata associated with a user action; adetection of a refund; and a detection of a support ticket or request.8. A system for processing electronic payments, the system comprising:at least one processor; and a memory storing instructions that, whenexecuted by the at least one processor, cause the system to performoperations comprising, at least: obtaining training data relating toprior purchase transactions processed via the electronic paymentprocessing system, the training data including data derived fromrespective checkout pages presented by respective interfaces of theelectronic payment processing system in respective checkout flows of theprior purchase transactions; extracting one or more features from thetraining data, the one or more features associated with a negative useraction invoked in relation to at least one of the prior purchasetransactions; for a real-time purchase transaction not included in thetraining data, using the extracted one or more features associated withthe negative user action to derive a transactional behavior scoreindicative of a probability that the real-time purchase transaction willinvoke the negative user action taken by a user in relation to thereal-time purchase transaction; based on the transactional behaviorscore, causing a dynamic optimization, during a respective checkout flowof the real-time purchase transaction, of a user interface presenting acheckout page in the respective checkout flow, the dynamic optimizationof the user interface including insertion in the respective checkoutflow of a targeted remedial action to reduce a probability that thereal-time purchase transaction will invoke the negative user actiontaken in relation to the real-time purchase transaction; receiving arequest to invoke the targeted remedial action; and in response toreceiving the request to invoke the targeted remedial action, triggeringthe targeted remedial action.
 9. The system of claim 8, wherein thetraining data is obtained from a data source, the data source confinedto data contained in or associated with the respective checkout pages.10. The system of claim 9, wherein the invoked negative user actionincludes one or more of a refund request, a chargeback request, and areturn request.
 11. The system of claim 10, wherein the operationsfurther comprise confining the training data to information extractedfrom the respective checkout pages and associated with the invokednegative user action.
 12. The system of claim 8, wherein the trainingdata includes a data structure, the data structure including a firstuser interaction section and a checkout page data section.
 13. Thesystem of claim 12, wherein the first user interaction section includesdata relating to one or more of: a time period between a loading of thecheckout page and a taking or completion of a payment action; a numberof times or frequency a customer viewed the checkout page before takingor completing a payment action; a number of typos or other mis-entriescorrected prior to taking or completing a payment action; a detection ofan omitted field completed prior to taking or completing a paymentaction; a number, type, or frequency of a mouse movement; a detection ofa payment denial; a detection of a decline of a payment instrument; adetection of a substitution of the payment instrument; an IP addressassociated with a prior purchase or the real-time purchase; and a localuser time of the prior purchase or the real-time purchase.
 14. Thesystem of claim 12, wherein the checkout page data section includes datarelating to one or more of: a detection of a first-time customer; adetection of a repeat customer; a time period of an interval between aprior purchase and the real-time purchase, assessed relative to acustomer average for the interval; a user cart size associated with thereal-time purchase, assessed relative to an average user cart size for apurchase; a locality or default currency of a payment instrument to givean indication of a customer's location relative to a merchant; userorder or shipping API data; a selection of a shipping service orshipping rate; embedded metadata associated with a user action; adetection of a refund; and a detection of a support ticket or request.15. A non-transitory machine-readable medium comprising instructionswhich, when read by a machine, cause the machine to perform operationsfor processing payments made via an electronic payment processingsystem, the operations comprising, at least: obtaining training datarelating to prior purchase transactions processed via the electronicpayment processing system, the training data including data derived fromrespective checkout pages presented by respective interfaces of theelectronic payment processing system in respective checkout flows of theprior purchase transactions; extracting one or more features from thetraining data, the one or more features associated with a negative useraction invoked in relation to at least one of the prior purchasetransactions; for a real-time purchase transaction not included in thetraining data, using the extracted one or more features associated withthe negative user action to derive a transactional behavior scoreindicative of a probability that the real-time purchase transaction willinvoke the negative user action taken by a user in relation to thereal-time purchase transaction; based on the transactional behaviorscore, causing a dynamic optimization, during a respective checkout flowof the real-time purchase transaction, of a user interface presenting acheckout page in the respective checkout flow, the dynamic optimizationof the user interface including insertion in the respective checkoutflow of a targeted remedial action to reduce a probability that thereal-time purchase transaction will invoke the negative user actiontaken in relation to the real-time purchase transaction; receiving arequest to invoke the targeted remedial action; and in response toreceiving the request to invoke the targeted remedial action, triggeringthe targeted remedial action.
 16. The medium of claim 15, wherein thetraining data is obtained from a data source, the data source confinedto data contained in or associated with the respective checkout pages.17. The medium of claim 16, wherein the invoked negative user actionincludes one or more of a refund request, a chargeback request, and areturn request.
 18. The medium of claim 17, wherein the operationsfurther comprise confining the training data to information extractedfrom the respective checkout pages and associated with the invokednegative user action.
 19. The medium of claim 15, wherein the trainingdata includes a data structure, the data structure including a firstuser interaction section and a first checkout page data section.
 20. Themedium of claim 19, wherein the first user interaction section includesdata relating to one or more of: a time period between a loading of thecheckout page and a taking or completion of a payment action; a numberof times or frequency a customer viewed the checkout page before takingor completing a payment action; a number of typos or other mis-entriescorrected prior to taking or completing a payment action; a detection ofan omitted field completed prior to taking or completing a paymentaction; a number, type, or frequency of a mouse movement; a detection ofa payment denial; a detection of a decline of a payment instrument; adetection of a substitution of the payment instrument; an IP addressassociated with a prior purchase or the real-time purchase; and a localuser time of the prior purchase or the real-time purchase; and whereinthe checkout page data section includes data relating to one or more of:a detection of a first-time customer; a detection of a repeat customer;a time period of an interval between a prior purchase and the real-timepurchase, assessed relative to a customer average for the interval; auser cart size associated with the real-time purchase, assessed relativeto an average user cart size for a purchase; a locality or defaultcurrency of a payment instrument to give an indication of a customer'slocation relative to a merchant; user order or shipping API data; aselection of a shipping service or shipping rate; embedded metadataassociated with a user action; a detection of a refund; and a detectionof a support ticket or request.